Is PyTables any option for you ? -- Sebastian Haase
On Mon, Jul 27, 2009 at 12:37 PM, Kim Hansen<[email protected]> wrote: >> >> I think it would be quite complicated. One fundamental "limitation" of >> numpy is that it views a contiguous chunk of memory. You can't have one >> numpy array which is the union of two memory blocks with a hole in >> between, so if you slice every 1000 items, the underlying memory of the >> array still needs to 'view' the whole thing. I think it is not possible >> to support what you want with one numpy array. > > Yes, I see the problem in getting the same kind of reuse of objects > using simple indexing. For my specific case, I will just allocate a > new array as containing a copy of every 100th element and return this > array. It will basically give me the same result as the original > recarray is for read-only purposes only. This will be very simple > implement for the specific cases I have > >> >> I think the simple solution really is to go 64 bits, that's exactly the >> kind of things it is used for. If your machine is relatively recent, it >> supports 64 bits addressing. >> > The machine is new and shiny with loads of processing power and many > TB of HDD storage. I am however bound to 32 bits Win XP OS as there > are some other costum made third-party and very expensive applications > running on that machine (which generate the large files I analyze), > which can only run on 32 bits, oh well.... > > Cheers, > > Kim > > >> cheers, >> >> David >> _______________________________________________ >> NumPy-Discussion mailing list >> [email protected] >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
